DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
The examiner encourages Applicant to submit an authorization to communicate with the examiner via the Internet by making the following statement (from MPEP 502.03):
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Please note that the above statement can only be submitted via Central Fax, Regular postal mail, or EFS Web (PTO/SB/439).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 14 is rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter.
During examination, the claims must be interpreted as broadly as their terms reasonably allow. In re American Academy of Science Tech Center, 367 F.3d 1359, 1369, 70 U.S.P.Q.2d 1827, 1834 (Fed. Cir. 2004). Independent claim 14 recites a “computer-readable storage medium” which is not comprehensively defined by the specification. The broadest reasonable interpretation of a claim drawn to a “computer-readable storage medium” covers forms of transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. Transitory propagating signals are non-statutory subject matter. In re Nuijten, 500 F.3d 1346, 1356-57, 84 U.S.P.Q.2d 1495, 1502 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter). See also Subject Matter Eligibility of Computer Readable Media, 1351 Off. Gaz. Pat. Office 212 (Feb. 23, 2010). Examiner suggests adding the word “non-transitory.”
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US 20230068880) in view of Haghighat at al. (US 20210263779).
As per claim 1, Liu teaches the invention substantially as claimed including a resource processing method, comprising:
acquiring a target container of a target function ([0056], When a new TEE-enabled secure container is instantiated, the controller (610) can pre-schedule it to some secure execution of the functions from possible clients represented by attestation delegators beforehand), wherein the target container is configured to run the target function ([0053], Upon receiving the request with an SEC flag, the FaaS infrastructure informs the controller of the container cluster orchestrator to schedule this function into one or more TEE-enabled secure containers; and [0059], many containers will have the same measures (as they have the same hardware configuration and software stack) and the attestation delegator can cache different measures for different function interpreters or executors to accelerate the remote attestation procedure);
Liu fails to specifically teach, acquiring a current resource already allocated to the target container; regulating the current resource to a target resource of the target container, wherein the target resource is determined based on the target function; and running, based on the target resource, the target function in the target container.
However, Haghighat teaches, acquiring a current resource already allocated to the target container ([0185], Block 664 determines whether the function uses and/or requires access to one or more unavailable resources and/or features. For example, block 664 may determine that a resource or a capability is unavailable when the resource or capability is not available at a user level, and/or not available to a container);
regulating the current resource to a target resource of the target container, wherein the target resource is determined based on the target function ([0185], Block 664 determines whether the function uses and/or requires access to one or more unavailable resources and/or features. For example, block 664 may determine that a resource or a capability is unavailable when the resource or capability is not available at a user level, and/or not available to a container; and [0186], block 666 enumerates the needed resources and/or capabilities for which user level access is needed... Block 668 adds the enumerated resources and/or capabilities. For example, block 668 temporarily adds the enumerated resources and/or capabilities to the container of the function); and
running, based on the target resource, the target function in the target container ([0186], Block 670 enables user level access to the enumerated resources and/or capabilities).
Liu and Haghighat are analogous because they are each related to scheduling functions on containers. Liu teaches a method of container assignment for functions based on the function’s characteristics:
Abstract: a method obtains an application program comprising a first set of one or more functions for execution within a secure execution area of a function-based service framework and a second set of one or more functions for execution within a non-secure execution area of the function-based service framework. A client attests an attestation delegator and the attestation delegator attests one or more secure containers prior to receipt of a function execution request to execute a function in the function-based service framework; and
[0071]: Attestation delegator 606 provisions the function body together with its input to the chosen container via the secure connection).
Haghighat teaches a method of dynamic container scaling and management based on monitoring information:
[0309]: the server 1302 may reduce resource contention by distributing first-third functions 1312, 1314, 1316 to allow the first-third functions 1312, 1314, 1316, to have first-third resource allocations 1326, 1328, 1330 that are sufficient for execution. Further, in some embodiments, fourth-sixth functions 1318, 1320, 1322, which are executing on the first-third compute nodes 1304a-1304c, may have allocation amounts of the first-third resources that do not impair access to the first-third resources by the first-third functions 1312, 1314, 1316; and
[0316]: the server 1302 may receive requests to execute the first-third functions 1312, 1314, 1316 from triggering devices 1306, 1308, 1310 and distribute the first-third functions 1312, 1314, 1316 to various ones of the compute nodes 1304a-1304c to avoid resource contention. In some embodiments, the server 1302 may already have scheduled fourth-sixth functions 1318, 1320, 1322 (e.g., non-FaaS functions) that are in execution at compute nodes 1304a-1304c).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention that based on the combination, the Liu’s container management system would be modified with Haghighat’s scaling mechanism resulting in a system that manages container resources for functions based on monitoring information. Therefore, it would have been obvious to combine the teachings of Liu and Haghighat.
As per claim 2, Haghighat teaches, wherein the regulating the current resource to the target resource of the target container comprises:
reducing the current resource to the target resource, in response to the current resource not being fully utilized for the target container ([0922], A function's resources are ‘reclaimed’ if the function's time-to-live has expired, or if other considerations such as its weighted measure for being kept running, has fallen below some threshold; and [0935], A probability evaluator 3685 of orchestrator 3680 may generate a decision to keep container 3670 for function Y 3675 warm, which may hinge upon a dynamic evaluation of such a probability P 3660; or more generally, a container reclaim rate for functions of type Y may be varied according to this probability P 3660. By adapting the reclaim rates and decisions based on both recent and historical assessments of probability P 3660, an overall objective that is both adaptive to recent information and also load-balanced with respect to long term information, may be attained).
As per claim 3, Haghighat teaches, wherein after reducing the current resource to the target resource, the method further comprises:
increasing the target container allocated to a virtual machine from an original number to a target number based on the target resource ([0161], The orchestrator 404 may profile data such as for example resource needs and/or demand profiles of functions described with respect to the embodiments of FIGS. 13A-13C, static and dynamic profile information of a function as described with respect to the embodiments of FIGS. 24A-24B, and dynamic profiles as described below with respect to FIGS. 40A-40B; [0185], Block 664 determines whether the function uses and/or requires access to one or more unavailable resources and/or features. For example, block 664 may determine that a resource or a capability is unavailable when the resource or capability is not available at a user level, and/or not available to a container; and [0186], block 666 enumerates the needed resources and/or capabilities for which user level access is needed).
As per claim 4, Haghighat teaches, wherein the regulating the current resource to the target resource of the target container comprises:
acquiring an average historical resource used by the target function during a historical period, wherein historical resources comprise the average historical resource ([0162], the telemetry manager 416 may monitor and record resource needs of functions, for example as metrics data (e.g., cache usage over time); and [0313], the server 1302 may determine whether one or more resource allocations needed by each of the first-third functions 1312, 1314, 1316 are above a threshold. If so, the server 1302 may label the one or more resources as being sensitive resources. The threshold may correspond to an average historical resource availability at the first-third compute nodes 1304a-1304c); and
determining the target resource based on the historical period and the average historical resource ([0162], the telemetry manager 416 may monitor and record resource needs of functions, for example as metrics data (e.g., cache usage over time); and [0314], server 1302 may further schedule the first-third functions 1312, 1314, 1316 based on the speculatively determined first-third resources to execute on different ones of the compute nodes 1304a-1304c and/or at different timings to avoid resource conflicts. The server 1302 may therefore reduce latency and enhance completion rates of the first-third functions 1312, 1314, 1316).
As per claim 5, Haghighat teaches, further comprising:
acquiring a target parameter of the target function, wherein the target parameter is used to determine an over-commitment degree of the target resource ([0313], the server 1302 may determine whether one or more resource allocations needed by each of the first-third functions 1312, 1314, 1316 are above a threshold. If so, the server 1302 may label the one or more resources as being sensitive resources; and [0320], The sensitive resource requirement is the sensitive resource requirement of the function);
the determining the target resource based on the historical period and the average historical resource comprises:
determining the target resource based on the historical period, the average historical resource and the target parameter ([0162], the telemetry manager 416 may monitor and record resource needs of functions, for example as metrics data (e.g., cache usage over time); and [0314], server 1302 may further schedule the first-third functions 1312, 1314, 1316 based on the speculatively determined first-third resources to execute on different ones of the compute nodes 1304a-1304c and/or at different timings to avoid resource conflicts. The server 1302 may therefore reduce latency and enhance completion rates of the first-third functions 1312, 1314, 1316).
As per claim 6, Haghighat teaches, further comprising:
acquiring profile data of the target function ([0855], function analyzer 3206 may determine the monikers based on the metadata, and the function F.sub.1 may determine a type of container having a soft affinity with the monikers to determine whether the container may include at least part of the F.sub.1 dataset as well as other resources that may be needed during execution), wherein the profile data comprises a maximum number of target containers allowed to be allocated to a virtual machine when the target container of the target function is co-located with a container of another function on the virtual machine ([0406], some embodiments may identify first-N batchable function requests 1608a-1608n that each request execution of a batchable function. The first-N batchable function requests 1608a-1608n may be batched together, and anticipatorily scheduled so that the batchable functions execute in a same container 1614a, rather than in different containers); and
determining, based on the profile data, a number of target containers allocated to the virtual machine(s) ([0855], function analyzer 3206 may determine the monikers based on the metadata, and the function F.sub.1 may determine a type of container having a soft affinity with the monikers to determine whether the container may include at least part of the F.sub.1 dataset as well as other resources that may be needed during execution; and [0856], orchestrator 3202 defines an association between the function F.sub.1 and the dataset that serves as a latent state to guide storage, cache, and communication efficient scheduling by attracting the function F.sub.1 towards a container (e.g., the first container as is described below) that has a highest likelihood of possessing local, warm copies of the data the function F.sub.1 will most likely to need during execution. Moreover, the above enhancement may be achieved without breaching a serverless abstraction).
As per claim 7, Haghighat teaches, wherein the acquiring the profile data of the target function comprises:
acquiring the profile data of the target function ([0855], function analyzer 3206 may determine the monikers based on the metadata, and the function F.sub.1 may determine a type of container having a soft affinity with the monikers to determine whether the container may include at least part of the F.sub.1 dataset as well as other resources that may be needed during execution), in response to the virtual machine being located in a first target area ([0169], orchestrator 404 may determine and record the functions that are running on all servers under its control and their states. The orchestrator 404 may also be a collection of distributed orchestrators, each responsible for a subset of FaaS servers. When a new function is to be placed for execution, the orchestrator 404 may query the ML/AI Advisor and seek guidance for placement of the function).
As per claim 14, this is the “computer-readable storage medium claim” corresponding to claim 1 and is rejected for the same reasons. The same motivation used in the rejection of claim 1 is applicable to the instant claim.
As per claim 20, Liu teaches the invention substantially as claimed including a resource processing system, comprising:
a processor ([0085], system 900 includes a central processing unit (CPU) 901); and
a memory, connected with the processor ([0085], system 900 includes a central processing unit (CPU) 901 which performs various appropriate acts and processing, based on a computer program instruction stored in a read-only memory (ROM) 902 or a computer program instruction loaded from a storage unit 908 to a random access memory (RAM) 903) and configured to provide, to the processor, instructions for processing the following processing steps:
acquiring a target container of a target function ([0056], When a new TEE-enabled secure container is instantiated, the controller (610) can pre-schedule it to some secure execution of the functions from possible clients represented by attestation delegators beforehand), wherein the target container is configured to run the target function ([0053], Upon receiving the request with an SEC flag, the FaaS infrastructure informs the controller of the container cluster orchestrator to schedule this function into one or more TEE-enabled secure containers; and [0059], many containers will have the same measures (as they have the same hardware configuration and software stack) and the attestation delegator can cache different measures for different function interpreters or executors to accelerate the remote attestation procedure)
Liu fails to specifically teach, acquiring a current resource already allocated to the target container; regulating the current resource to a target resource of the target container, wherein the target resource is obtained based on a historical resource that is used by the target function during a historical period; and running, based on the target resource, the target function in the target container..
However, Haghighat teaches, acquiring a current resource already allocated to the target container ([0185], Block 664 determines whether the function uses and/or requires access to one or more unavailable resources and/or features. For example, block 664 may determine that a resource or a capability is unavailable when the resource or capability is not available at a user level, and/or not available to a container);
regulating the current resource to a target resource of the target container ([0185], Block 664 determines whether the function uses and/or requires access to one or more unavailable resources and/or features. For example, block 664 may determine that a resource or a capability is unavailable when the resource or capability is not available at a user level, and/or not available to a container; and [0186], block 666 enumerates the needed resources and/or capabilities for which user level access is needed... Block 668 adds the enumerated resources and/or capabilities. For example, block 668 temporarily adds the enumerated resources and/or capabilities to the container of the function), wherein the target resource is obtained based on a historical resource that is used by the target function during a historical period ([0161], The Orchestrator 404 may also include the following sub-components: telemetry manager 416; and [0162], the telemetry manager 416 may monitor and record resource needs of functions, for example as metrics data (e.g., cache usage over time); and [0314], server 1302 may further schedule the first-third functions 1312, 1314, 1316 based on the speculatively determined first-third resources to execute on different ones of the compute nodes 1304a-1304c and/or at different timings to avoid resource conflicts. The server 1302 may therefore reduce latency and enhance completion rates of the first-third functions 1312, 1314, 1316); and
running, based on the target resource, the target function in the target container ([0186], Block 670 enables user level access to the enumerated resources and/or capabilities).
The same motivation used in the rejection of claim 1 is applicable to the instant claim.
Claims 8-12 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Liu-Haghighat as applied to independent claim 1 and in further view of Chen et al. (US 20220334870).
As per claim 8, Haghighat teaches, further comprising:
monitoring the target container to obtain a first monitoring result ([0161], The Orchestrator 404 may also include the following sub-components: telemetry manager 416; and [0162], the telemetry manager 416 may monitor and record resource needs of functions, for example as metrics data (e.g., cache usage over time), and/or demand profiles as described in the embodiments of FIGS. 13A-13C, background performance monitoring and specific performance telemetry arising during execution of a function as discussed with respect to the embodiments FIGS. 7A-7C, out-of-band (OOB) telemetry as described with respect to the embodiments FIG. 22, telemetry and profile information as described with respect to the embodiments of FIGS. 24A-24B and telemetry information as described with respect to the embodiments of FIGS. 36A-36B).
The combination of Liu-Haghighat fails to specifically teach, performing migration processing or isolation processing on the target container, in response to the first monitoring result being used to indicate a performance degradation of the target function.
However, Chen teaches, performing migration processing or isolation processing on the target container, in response to the first monitoring result being used to indicate a performance degradation of the target function ([0029], the illustrative embodiments recognize and take into account that a container for containers for an application can be migrated within a cloud in response to degradation and application performance).
The combination of Liu-Haghighat and Chen are analogous because they are each related to scheduling functions on containers. Liu teaches a method of container assignment for functions based on the function’s characteristics. Haghighat teaches a method of dynamic container scaling and management based on monitoring information. Chen teaches a method of container management including dynamic container migration based on monitoring information:
Abstract: A method, apparatus, system, and computer program product for container migration. A set of processors operates to identify a set of containers for a set of applications for a migration using a set of application performance metrics. The set of processors operates to create a set of tasks following a migration strategy to move the set of containers for the set of applications identified for the migration from a set of current physical host computers to a set of target physical host computers using the set of application performance metric; and
[0029]: the illustrative embodiments recognize and take into account that a container for containers for an application can be migrated within a cloud in response to degradation and application performance; and
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention that based on the combination, the container management of the combination of Liu-Haghighat system would be modified with Chen’s migration mechanism resulting in a system that manages container resources for functions, including container migration, based on monitoring information. Therefore, it would have been obvious to combine the teachings of the combination of Liu-Haghighat and Chen.
As per claim 9, Haghighat teaches, further comprising:
monitoring the target container to obtain the first monitoring result ([0161], The Orchestrator 404 may also include the following sub-components: telemetry manager 416; and [0162], the telemetry manager 416 may monitor and record resource needs of functions, for example as metrics data (e.g., cache usage over time), and/or demand profiles as described in the embodiments of FIGS. 13A-13C, background performance monitoring and specific performance telemetry arising during execution of a function as discussed with respect to the embodiments FIGS. 7A-7C, out-of-band (OOB) telemetry as described with respect to the embodiments FIG. 22, telemetry and profile information as described with respect to the embodiments of FIGS. 24A-24B and telemetry information as described with respect to the embodiments of FIGS. 36A-36B), in response to a virtual machine to which the target container is allocated being located in a second target area ([0169], orchestrator 404 may determine and record the functions that are running on all servers under its control and their states. The orchestrator 404 may also be a collection of distributed orchestrators, each responsible for a subset of FaaS servers. When a new function is to be placed for execution, the orchestrator 404 may query the ML/AI Advisor and seek guidance for placement of the function).
As per claim 10, Haghighat teaches, wherein the monitoring the target container to obtain the first monitoring result comprises:
acquiring a delay duration for the target function in responding to a target request in the target container ([0168], The ML models may reveal that certain collections of functions that are executed simultaneously on the same system will result in unusually poor execution characteristics (e.g., anomalies, high latency executions, etc.)); and
determining that the first monitoring result is used to indicate the performance degradation of the target function, in response to the delay duration being greater than a target duration ([0168], ML/AI Advisor 420 may automatically learn from an enormous amount of past information collected by the telemetry manager 416 and processed by the profile manager 418...The ML models may reveal that certain collections of functions that are executed simultaneously on the same system will result in unusually poor execution characteristics (e.g., anomalies, high latency executions, etc.)).
As per claim 11, Chen teaches, wherein the performing the migration processing on the target container comprises:
migrating the target container from an original virtual machine to a target virtual machine, wherein the target virtual machine comprises at least one of following:
a virtual machine with a usage rate below a target threshold,
a virtual machine already allocated with a same container as the target container, and
a virtual machine located in a third target area without resource over-commitment ([0029], moving a container or containers for an application to another server computer with greater resource availability; [0131], identifying a set of containers for a set of applications for a migration using a set of application performance metrics (step 700). The process creates a set of tasks following a migration strategy to move the set of containers for the set of applications identified for the migration from a set of current physical host computers to a set of target physical host computers using the set of application performance metrics (step 702); and [0132], The process moves the set of containers for the set of applications from the set of current physical host computers to the set of target physical host computers using the set of tasks following the migration strategy, wherein application level performance is increased (step 704).).
As per claim 12, Chen teaches, wherein after performing the migration processing or the isolation processing on the target container, the method further comprises:
monitoring the target container subjected to the migration processing or the isolation processing, to obtain a second monitoring result ([0029], The illustrative embodiments recognize and take into account that performance metrics for the containers can be collected and analyzed and that the configuration of the containers can be changed to improve the performance of the containers); and
determining that the target function is in an abnormal state, in response to the second monitoring result being used to indicate the performance degradation of the target function ([0029], the illustrative embodiments recognize and take into account that a container for containers for an application can be migrated within a cloud in response to degradation and application performance).
Claims 13, 15-16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Haghighat at al. (US 20210263779).
As per claim 13, Haghighat teaches the invention substantially as claimed including a resource processing method, comprising:
determining a target area at which a virtual machine is located in a virtual machine cluster ([0169], orchestrator 404 may determine and record the functions that are running on all servers under its control and their states. The orchestrator 404 may also be a collection of distributed orchestrators, each responsible for a subset of FaaS servers; and [0889], A caller field 3302 may be used to identify the source of the function invocation (...geographic area, etc.). A caller moniker may be determined from the caller field 3302 to describe an ..location ... of the source);
determining a target container of a target function based on the target area ([0855], function analyzer 3206 may determine the monikers based on the metadata, and the function F.sub.1 may determine a type of container having a soft affinity with the monikers to determine whether the container may include at least part of the F.sub.1 dataset as well as other resources that may be needed during execution), and the target container is configured to run the target function ([0855], function analyzer 3206 may determine the monikers based on the metadata, and the function F.sub.1 may determine a type of container having a soft affinity with the monikers to determine whether the container may include at least part of the F.sub.1 dataset as well as other resources that may be needed during execution);
regulating a current resource already allocated to the target container to a target resource of the target container ([0185], Block 664 determines whether the function uses and/or requires access to one or more unavailable resources and/or features. For example, block 664 may determine that a resource or a capability is unavailable when the resource or capability is not available at a user level, and/or not available to a container; and [0186], block 666 enumerates the needed resources and/or capabilities for which user level access is needed... Block 668 adds the enumerated resources and/or capabilities. For example, block 668 temporarily adds the enumerated resources and/or capabilities to the container of the function), wherein the target resource is determined based on the target function ([0185], Block 664 determines whether the function uses and/or requires access to one or more unavailable resources and/or features. For example, block 664 may determine that a resource or a capability is unavailable when the resource or capability is not available at a user level, and/or not available to a container; and [0186], block 666 enumerates the needed resources and/or capabilities for which user level access is needed... Block 668 adds the enumerated resources and/or capabilities. For example, block 668 temporarily adds the enumerated resources and/or capabilities to the container of the function); and
running, based on the target resource, the target function in the target container ([0186], Block 670 enables user level access to the enumerated resources and/or capabilities).
Haghighat fails to specifically teach, wherein the target container of the target function is allowed to be allocated to the virtual machine. However, it would have been obvious to one of ordinary skill in the art to include this step because Haghighat teaches an attestation token to validate functions. ([0150], A container that is run in its own virtual machine is referred to as a virtual container...function code 100 gets executed on the CSP's physical infrastructure/Edge/IoT device and underlying virtualized containers; and [0178], if a verification module 628 in the OS 612 and/or a verification module 630 in the VMM 616 determine that the security attestation token 606 is valid, the first function 602 is permitted to use the features 614, 618, 622, and 626 (e.g., corresponding to the user level capabilities)).
As per claim 15, Haghighat teaches, wherein the target area comprises a planed zone, a mixed zone, and a control zone ([0169], orchestrator 404 may determine and record the functions that are running on all servers under its control and their states. The orchestrator 404 may also be a collection of distributed orchestrators, each responsible for a subset of FaaS servers. When a new function is to be placed for execution, the orchestrator 404 may query the ML/AI Advisor and seek guidance for placement of the function);
the determining the target area at which the virtual machine is located in the virtual machine cluster comprises:
determining the target area is in one of the planned zone, the mixed zone, and the control zone ([0169], orchestrator 404 may determine and record the functions that are running on all servers under its control and their states. The orchestrator 404 may also be a collection of distributed orchestrators, each responsible for a subset of FaaS servers. When a new function is to be placed for execution, the orchestrator 404 may query the ML/AI Advisor and seek guidance for placement of the function).
As per claim 16, Haghighat teaches, wherein the target container of the target function being allowed to be allocated to the virtual machine comprises:
acquiring profile data of the target function in response to a virtual machine being located in a first target area ([0169], orchestrator 404 may determine and record the functions that are running on all servers under its control and their states. The orchestrator 404 may also be a collection of distributed orchestrators, each responsible for a subset of FaaS servers. When a new function is to be placed for execution, the orchestrator 404 may query the ML/AI Advisor and seek guidance for placement of the function);
wherein the profile data comprises a maximum number of target containers allowed to be allocated to the virtual machine when the target container of the target function is co-located with a container of another function on the virtual machine ([0406], some embodiments may identify first-N batchable function requests 1608a-1608n that each request execution of a batchable function. The first-N batchable function requests 1608a-1608n may be batched together, and anticipatorily scheduled so that the batchable functions execute in a same container 1614a, rather than in different containers).
As per claim 19, this is the “computer-readable storage medium claim” corresponding to claim 13 and is rejected for the same reasons. The same motivation used in the rejection of claim 13 is applicable to the instant claim.
Claims 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Haghighat as applied to independent claim 13 and in further view of Chen et al. (US 20220334870).
As per claim 17, Haghighat teaches, wherein the regulating the current resource already allocated to the target container to the target resource of the target container comprises:
monitoring the target container to obtain a first monitoring result ([0161], The Orchestrator 404 may also include the following sub-components: telemetry manager 416; and [0162], the telemetry manager 416 may monitor and record resource needs of functions, for example as metrics data (e.g., cache usage over time), and/or demand profiles as described in the embodiments of FIGS. 13A-13C, background performance monitoring and specific performance telemetry arising during execution of a function as discussed with respect to the embodiments FIGS. 7A-7C, out-of-band (OOB) telemetry as described with respect to the embodiments FIG. 22, telemetry and profile information as described with respect to the embodiments of FIGS. 24A-24B and telemetry information as described with respect to the embodiments of FIGS. 36A-36B), in response to a virtual machine to which the target container is allocated being located in a second target area ([0169], orchestrator 404 may determine and record the functions that are running on all servers under its control and their states. The orchestrator 404 may also be a collection of distributed orchestrators, each responsible for a subset of FaaS servers. When a new function is to be placed for execution, the orchestrator 404 may query the ML/AI Advisor and seek guidance for placement of the function).
Haghighat fails to specifically teaches, performing migration processing or isolation processing on the target container, in response to the first monitoring result being used to indicate a performance degradation of the target function.
However, Chen teaches, performing migration processing or isolation processing on the target container, in response to the first monitoring result being used to indicate a performance degradation of the target function ([0029], the illustrative embodiments recognize and take into account that a container for containers for an application can be migrated within a cloud in response to degradation and application performance).
Haghighat and Chen are analogous because they are each related to scheduling functions on containers. Haghighat teaches a method of dynamic container scaling and management based on monitoring information. Chen teaches a method of container management including dynamic container migration based on monitoring information:
Abstract: A method, apparatus, system, and computer program product for container migration. A set of processors operates to identify a set of containers for a set of applications for a migration using a set of application performance metrics. The set of processors operates to create a set of tasks following a migration strategy to move the set of containers for the set of applications identified for the migration from a set of current physical host computers to a set of target physical host computers using the set of application performance metric; and
[0029]: the illustrative embodiments recognize and take into account that a container for containers for an application can be migrated within a cloud in response to degradation and application performance.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention that based on the combination, Haghighat’s container management system would be modified with Chen’s migration mechanism resulting in a system that manages container resources for functions, including container migration, based on monitoring information. Therefore, it would have been obvious to combine the teachings of Haghighat and Chen.
As per claim 18, Haghighat fails to specifically teach, wherein the regulating the current resource already allocated to the target container to the target resource of the target container further comprises: migrating the target container from an original virtual machine to a target virtual machine, wherein the target virtual machine comprises at least one of following: a virtual machine with a usage rate below a target threshold, a virtual machine already allocated with a same container as the target container, and a virtual machine located in a third target area without resource over- commitment.
However, Chen teaches, wherein the regulating the current resource already allocated to the target container to the target resource of the target container further comprises:
migrating the target container from an original virtual machine to a target virtual machine, wherein the target virtual machine comprises at least one of following:
a virtual machine with a usage rate below a target threshold,
a virtual machine already allocated with a same container as the target container, and
a virtual machine located in a third target area without resource over- commitment ([0029], moving a container or containers for an application to another server computer with greater resource availability; [0131], identifying a set of containers for a set of applications for a migration using a set of application performance metrics (step 700). The process creates a set of tasks following a migration strategy to move the set of containers for the set of applications identified for the migration from a set of current physical host computers to a set of target physical host computers using the set of application performance metrics (step 702); and [0132], The process moves the set of containers for the set of applications from the set of current physical host computers to the set of target physical host computers using the set of tasks following the migration strategy, wherein application level performance is increased (step 704).).
The same motivation used in the rejection of claim 17 is applicable to the instant claim.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is as follows:
Inventor
Application No.
Teaches
Sivasubramanian et al.
US 20150378764 A1
Teaches capacity planning for virtual machines:
Abstract, An access data collector collects access assignment data characterizing active access assignment operations of a hypervisor in assigning host computing resources among virtual machines for use in execution of the virtual machines. Then, a capacity risk indicator calculator calculates a capacity risk indicator characterizing a capacity risk of the host computing resources with respect to meeting a prospective capacity demand of the virtual machines, based on the access assignment data
Nemoto et al.
US8898570B2
Teaches virtual machine management based on resource requirements:
Abstract, virtual machine management/monitoring service can be configured to automatically monitor and implement user-defined (e.g., administrator-defined) configuration policies with respect to virtual machine and application resource utilization
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/MELISSA A HEADLY/Examiner, Art Unit 2197
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